DataRobot-Research/drdocs huggingface dataset:
# Version 7.1.2 {: #version-712 }

_August 2, 2021_

The DataRobot v7.1.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.1.0 release notes for:

* [Features introduced in v7.1.0](v7.1.0-aml)

## Updated language localization {: #updated-language-localization }

Localization of the documentation has been updated to include Japanese content for all new 7.1 features.

To change languages, open your [user settings](getting-help) and select a language for your session. The selection remains until you reset it.

![](images/languages-set-pt-jp.png)

## Issues fixed in v7.1.2 {: #issues-fixed-in-v712 }

The following issues have been fixed since Enterprise release v7.1.1:

### Enterprise {: #enterprise }

* EP-1226: Fixes an issue where `patroni` installs failed because nodes were not syncing.
* EP-1345: Fixes an issue that caused a KeyError during DB migration.

### Platform {: #platform }

* PRODSEC-234: Prevents containers from acquiring new privileges.
* XAI-4238: Fixes an issue when loading certain models with SHAP explainers that were built in DataRobot version 5.2.2, which failed to load after upgrading to version 6.3.

### Feature Discovery {: #feature-discovery }

* SAFER-3964: Fixes an issue where Feature Discovery projects created before DataRobot version 7.1 resulted in an error when running predictions.

### Time Series {: #time-series }

* TIME-8745: Allows `COMPUTE_TIMESERIES_AOT_THRESHOLD` to be configured from the environment.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
* Frozen thresholds are not supported.

* Blenders that contain monotonic models do not display the MONO label on the Leaderboard for OTV projects.

* When previewing predictions over time, the interval only displays for models that haven’t been retrained (for example, it won’t show up for models with the **Recommended for Deployment** badge).

* User models are supported from the Repository tab.

* If you configure long backtest durations, DataRobot will still build models, but will not run backtests in cases where there is not enough data. In these case, the backtest score will not be available on the Leaderboard.

* Timezones on date partition columns are ignored. Datasets with multiple time zones may cause issues. The workaround is to convert to a single time zone outside of DataRobot. Also there is no support for daylight savings time.

* Dates before 1900 are not supported. If necessary, shift your data forward in time.

* Leap seconds are not currently supported.
---
title: Predictions reference
description: Answers questions and provides tips for working with Predictions in DataRobot's Workbench.

---

# Predictions reference {: #predictions-reference }

??? faq "What types of experiments are supported for Workbench predictions?"
    Currently, Workbench supports predictions for binary classification and regression projects; it doesn't support time series or Feature Discovery projects. Because development is ongoing, see the [capability matrix](wb-capability-matrix) for a full list of supported capabilities.

??? faq "What prediction options are supported?"
    Currently, Workbench only supports the **Include input features** option. For additional prediction options, you can build models in Workbench, switch to DataRobot Classic, [deploy a model](deploy-model){ target=_blank }, and then [make batch predictions](batch-pred#make-predictions-with-a-deployment){ target=_blank }.

??? faq "How can I make predictions in DataRobot Classic?"
    You can make predictions on Workbench models in DataRobot classic, but only [from the Leaderboard](predict){ target=_blank } (via UI or API) or after [deploying the model](deploy-model){ target=_blank }. If you deploy the model, you can [make batch predictions](batch-pred#make-predictions-with-a-deployment){ target=_blank }.

    You can easily switch back and forth between Workbench and DataRobot Classic. Access Workbench from the top navigation bar in the application.

	![](images/wb-uc-faq-1.png)

	To return to DataRobot Classic to deploy your model and make predictions, in the top navigation bar, click **DataRobot Classic**, and then, in the dropdown list, click **Leave Workbench**.

	![](images/wb-uc-faq-2.png)

    From DataRobot Classic, you can [deploy models](deploy-model){ target=_blank } and [make predictions](predictions/index){ target=_blank }. You can see the Use Case name in the [Projects dropdown](manage-projects#projects-dropdown){ target=_blank } to identify your Workbench Use Case in the DataRobot Classic UI.

    ![](images/wb-pred-faq.png)

??? faq "Can I make predictions on training data?"
    You shouldn't make predictions on training data. It is possible to upload the training dataset and make predictions, but the results won’t always match those from the Leaderboard in DataRobot Classic. If you want to make predictions on training data, you can [make Leaderboard predictions](predict){ target=_blank } back in DataRobot Classic.
---
title: Build models
description: Introduces the phases of building models, including Advanced options, building models, and managing projects.

---

# Build models {: #build-models }

These sections describe aspects of preparing to build, building, and managing models and projects:

Topic | Describes...
----- | ------
[Build models](build-basic/index) | Understand elements of the basic modeling workflow.
[Advanced options](adv-opt/index) | Set advanced modeling parameters prior to building.
**Reference** | :~~:
[Manage projects](manage-projects) | Use the project control center to manage models and projects, as well as export data.
[Generate AI Report](generate-ai-report) | Create a report of modeling results and insights.
[Export charts and data](export-results) | Download created insights.
[Exploratory Data Analysis](eda-explained) | Details of Exploratory Data Analysis (EDA), phases 1  and 2.
[Data partitioning and validation](data-partitioning) | Understand validation types and data partitioning methods.
[Modeling process details](model-ref) | Bits and pieces of the initial model building process.
[Optimization metrics](opt-metric) | Short descriptions of metrics available for model building.
[Worker Queue](worker-queue) | Manage queued and processing models.
---
title: Open source and User models
description: Explains the deprecation of Open Source and User models.

---

# Open source and User models {: #open-source-and-user-models }

As of November 2022, DataRobot disabled all models containing User/Open source (“user”) tasks. See the [release announcement](june2022-announce#user-open-source-models-deprecated-and-soon-disabled) for full information on identifying these models. Use the [Composable ML](cml/index) functionality to create custom models.

There are native solutions for all currently supported open source models, and disabling and replacing the deprecated model types addresses any potential security concerns.

To determine whether your model is of the open source/User type, open the blueprint, where you will see the task name:

![](images/rn-oss-deprecate.png)

Additionally, you can see the task listed in the model description on the Leaderboard.

## Deprecated and disabled functionality {: #deprecated-and-disabled-functionality }

The following table describes the stages of deprecation:

Stage | Impact | Timeline
----- |------- | --------
*Deprecated* | Existing models cannot be deployed, but predictions and insights can be computed. New models cannot be created. | 8/2022 - 11/ 2022
*Disabled* | Model data is read-only. Any pre-computed data can be viewed for reference and will be retained long-term for audit purposes. Any actions involving compute jobs are disabled (computing insights charts, etc.). | 11/2022 and beyond


=== "Managed AI Platform (SaaS)"
	Consider the dates below:

	**August 2022**

	* Open source and Usuer models, and any blenders that include these models, are **deprecated**.


	**November 2022**

	* Open source and User models, and any blenders that include these models, are **disabled**.


=== "Self-Managed AI Platform"
	Consider the dates below:

	* Open source and User models, and any blenders that include these models, will be **disabled** in release 9.0.
---
title: Speed vs Accuracy
description: Predictive accuracy often requires longer prediction runtime. The Speed vs Accuracy plot shows the runtime/accuracy tradeoff to help you choose the best model.

---

# Speed vs Accuracy {: #speed-vs-accuracy }

Predictive accuracy often comes at the price of increased prediction runtime. The **Speed vs Accuracy** analysis plot shows the tradeoff between runtime and predictive accuracy and helps you choose the best model with the lowest overhead. The **Speed vs Accuracy** display is based on the validation score, using the currently selected metric.

* The Y-axis lists the metric currently selected on the Leaderboard. To change metric, switch to the Leaderboard, change the metric via the **Metric** dropdown, and then return to **Speed vs Accuracy**.

* The X-axis displays the estimated time, in milliseconds, to make 1000 predictions. Total prediction times include a variety of factors and vary based on the implementation. Mouse over any point on the graph, or the model name in the legend to the right, to display the estimated time and the score.

![](images/model-speed-vs-accuracy.png)

!!! tip
    If you re-order the Leaderboard display, for example to sort by cross-validation score, the <b>Speed vs Accuracy</b> graph continues to plot the top 10 models based on validation score.
---
title: Unsupervised learning
description: Work with unlabeled data to build models in unsupervised mode (anomaly detection and clustering).

---

# Unsupervised learning {: #unsupervised-learning }

Typically DataRobot works with labeled data, using supervised learning methods for model building. With supervised learning, you specify a target (what you want to predict) and DataRobot builds models using the other features of your dataset to make that prediction.

DataRobot also supports *unsupervised learning* where no target is specified and the data is unlabeled. Instead of generating predictions as in supervised learning, unsupervised learning surfaces insights about patterns in your data, answering questions like "Are there anomalies in my data?" and "Are there natural clusters?"

These unsupervised learning strategies are described in the following sections:

| Topic | Describes... |
|---|---|
| [Anomaly detection](anomaly-detection) |  Use unsupervised learning to detect abnormalities in your dataset. |
| [Clustering](clustering) | Use unsupervised learning to group similar data and identify segments. |
# Version 7.1.1 {: #version-711 }

_June 19, 2021_

The DataRobot v7.1.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.1.0 release notes for:

* [Features introduced in v7.1.0](v7.1.0-aml)

## Issues fixed in v7.1.1 {: #issues-fixed-in-v711 }

The following issues have been fixed since Enterprise release v7.1.0:

### Enterprise {: #enterprise }

* EP-1315: Adds a missing `eks-mlops.yaml` example config in `installer/example-configs/docker`.
* EP-1303: Fixes an issue where the DataRobot install on azure instances fails the mongo version check.
* EP-1293: Updates the Platform Guide Installer Toolkit documentation for the 7.1 Install Guide.

### Platform {: #platform }

* SAFER-3796: Fixes an issue where the SQL Server Data Connection in the AI Catalog required authentication after saving credentials.

### Custom Models {: #custom-models }

* RAPTOR-5575: Adds _julia_ as a programming language type when creating custom execution environments.
* RAPTOR-5306: Fixes an issue where task names were not displayed in blueprint diagrams.

### Predictions {: #predictions }

* PRED-6005: Fixes an issue when updating a Batch Prediction Job Definition.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Stability
description: The Stability tab provides an at-a-glance summary of how well a model performs on different backtests, to understand whether a model is consistent across time.

---

# Stability {: #stability }

The **Stability** tab provides an at-a-glance summary of how well a model performs on different backtests. Use the results to  understand whether a model is consistent across time, helping to evaluate the process of training and measuring performance. To use the tab, which is available for all [date/time partitioning](ts-date-time) projects, first compute all backtests for the model (from the Leaderboard, click **Backtesting > Run**). To include the information for the holdout partition, unlock holdout. The backtesting information in this chart is the same as that available from the [**Model Info**](ts-date-time#view-summary-information) tab.

![](images/otv-stability.png)

The values in the chart represent the validation scores for each backtest and the holdout. Hover over a backtest or holdout to display the actual score and the range for the partition.

![](images/otv-stability-tooltip.png)

Changing the optimization metric from the Leaderboard changes the display, providing additional evaluation tools:

![](images/otv-stab-metric.png)
---
title: Custom model environments
description: How to set up an environment for custom inference models created in the Custom Model Workshop.

---

# Custom model environments {: #custom-model-environments }

To [create a custom inference model](custom-inf-model), you must select an environment that the model will use. An environment includes packages and language and system libraries used for models. You can select one of two types of environments:

Environment | Description
------------|------------
[Drop-in environments](drop-in-environments) | Contain web server Scoring Code and a `start_server.sh` file for the model to use. They are provided by DataRobot in the Custom Model Workshop.
[Custom environments](custom-environments)   | Do _not_ contain the Scoring Code and `start_server.sh` file, which instead must be provided in the folder of the custom model you intend to use with the environment. You can create your own environment in the Custom Model Workshop. You can also create a custom drop-in environment by including the Scoring Code and `start_server.sh` file in the environment folder.

By providing an environment separate from a custom model, DataRobot can build the environment for you. This allows you to reuse the environment for as many models as you want. It also provides the ability to provide a model by uploading a folder containing its code and model artifacts without providing web server Scoring Code and a `start_server.sh` file with every model.
---
title: Delete and restore projects
description: For administrators, how to permanently delete a project, or restore a project temporarily deleted by a user.
platform: self-managed-only

---

# Delete/restore projects {: #deleterestore-projects }

!!! info "Availability information"
    **Required permission:** Can delete/restore projects

As an administrator, you can delete and restore (or recover) projects. Deleting projects is a valuable tool that can help clear space for new projects.

When the project owner deletes a project, it is not _permanently_ deleted; only an administrator can permanently delete projects, or restore them if needed.

##  Permanently delete projects {: #permanently-delete-projects }

To permanently delete a project:

1. Expand the profile icon located in the upper right and click **Manage Deleted Projects**:

	![](images/manage-deletedprojects-dropdown.png)

	The **Manage Deleted Projects** tab shows all deleted projects for all users.

	![](images/perm-delete-project.png)

2. You can permanently delete one or more projects at a time, as follows:

    * Deleting one project at a time&mdash;click on the "X" icon for that project (shown above).
    * Deleting multiple projects at one time&mdash;select the checkbox for each project to delete, or from **Menu** choose **Select All** (to delete all projects).

     Then, from **Menu** choose **Permanently Delete Selected Projects**.

	![](images/perm-multidelete-projects.png)

   A warning message appears prompting you to confirm that you want to delete the project(s).

   ![](images/confirm-perm-delete-project.png)

3. Click to delete the project(s).

!!! note
        Once you delete a project from the <b>Manage Deleted Projects</b> page, it is permanently deleted and cannot be recovered.

##  Restore projects {: #restore-projects }

Any projects listed in the **Manage Deleted Projects** page can be restored.

1. Open the **Manage Deleted Projects** page (if not open).

     The **Manage Deleted Projects** tab shows all deleted projects for all users.

    ![](images/recover-deleted-projects.png)

2. You can restore one or more projects at a time, as follows:

    * Restoring one project at a time&mdash;click on the circular arrow pointing in counter-clockwise direction (as shown above)
    * Restoring multiple projects at one time&mdash;select the checkbox for each project to restore, or from **Menu** choose **Select All**  (to restore all projects).

    Then, from **Menu** choose **Recover Selected Projects**.

    A warning message appears prompting you to confirm that you want to restore the project(s).

    ![](images/confirm-project-restoration.png)

3. Click to restore the project(s). Restored projects will be immediately available, to the owner and to anyone sharing the project.
---
title: Time series advanced modeling
description: This topic provides deep-dive reference material for DataRobot time series modeling.

---

# Time series advanced modeling {: #time-series-advanced-modeling }

This section provides information for DataRobot time series modeling.

Topic | Describes...
----- | ------
[Time Series advanced options](ts-adv-opt) | The many advanced features available for customizing time series projects.
[Clustering advanced options](ts-cluster-adv-opt) | How to set the number of clusters DataRobot automatically discovers.
[Date/Time for time series](ts-date-time) | Non-default time formats and backtesting.
[Customizing time series projects](ts-customization)| How DataRobot calculates training partitions as well as changing default partitioning and window values.
---
title: Register DataRobot models
description: Register a model package in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages.

---

# Register DataRobot models from the Leaderboard {: #register-datarobot-models-from-the-Leaderboard }

The Model Registry is an organizational hub for the variety of models used in DataRobot. Models are registered as deployment-ready model packages; the registry lists each package available for use.

To add a DataRobot model trained with AutoML to the Model Registry:

1. Select the model from the Leaderboard.

2. Click **Predict > Deploy** and click **Add to Model Registry**.

    ![](images/reg-comp-1.png)

    You can later [deploy the model from the Model Registry](deploy-model#deploy-from-the-model-registry).
Consider the following when working with segmented modeling deployments:

* Time series segmented modeling deployments do not support data drift monitoring.
   
* Automatic retraining for segmented deployments that use clustering models is disabled; retraining must be done manually.

* Retraining can be triggered by accuracy drift in a Combined Model; however, it doesn't support monitoring accuracy in individual segments or retraining individual segments.

* Combined model deployments can include standard model challengers.
---
title: Version 7.1.x
description: DataRobot Release 7.1 release announcements index page.
---


# Version 7.1.x {: #version-71x }

Following are the AutoML, time series, and MLOps announcements for the features that make up DataRobot's 7.1.x releases, first released _June 14, 2021_.

* [v7.1.x AutoML](v7.1.0-aml)
* [v7.1.x time series](v7.1.0-ats)
* [v7.1.x MLOps](v7.1-mlops)
* [Maintenance release notes](7.1-maintenance/index) for DataRobot v7.1.x.
## Feature considerations {: #feature-considerations }

Consider the following when working with Scoring Code:

* Using Scoring Code in production requires additional development efforts to implement model management and model monitoring, which the DataRobot API provides out of the box.

* Exportable Java Scoring Code requires extra RAM during model building. As a result, to use this feature, you should keep your training dataset under 8GB. Projects larger than 8GB may fail due to memory issues. If you get an out-of-memory error, decrease the sample size and try again. The memory requirement _does not apply during model scoring_. During scoring, the only limitation on the dataset is the RAM of the machine on which the Scoring Code is run.

### Model support {: #model-support }

* Scoring Code is available for models containing only _supported_ built-in tasks. It is not available for [custom models](custom-inf-model) or models containing one or more [custom tasks](cml-custom-tasks).

* Scoring Code is not supported in multilabel projects.

*  Keras models do not support Scoring Code by default; however, support can be enabled by having an administrator activate the Enable Scoring Code Support for Keras Models feature flag. If enabled, note that these models are not compatible with Scoring Code for Android and Snowflake.

Additional instances in which Scoring Code generation is not available include:

* Naive Bayes

* Text tokenization involving the MeCab tokenizer

* Visual AI and Location AI

### Time series support {: #time-series-support }

* The following time series capabilities are not supported for Scoring Code:

    * Row-based / irregular data
    * Nowcasting (single forecast point)
    * Intramonth seasonality
    * Time series blenders
    * Autoexpansion
    * EWMA (Exponentially Weighted Moving Average)

* Scoring Code is not supported in time series binary classification projects.

* Scoring Code is not typically supported in time series anomaly detection models; however, it is supported for IsolationForest and some XGBoost-based anomaly detection model blueprints. For a list of supported time series blueprints, see the [Time series blueprints with Scoring Code support](#ts-sc-blueprint-support) note.

{% include 'includes/scoring-code-consider-ts.md' %}

### Prediction Explanations support {: #prediction-explanations-support }

Consider the following when working with Prediction Explanations for Scoring Code:

* To download Prediction Explanations with Scoring Code, you _must_ select **Include Prediction Explanations** during [Leaderboard download](sc-download-leaderboard#leaderboard-download) or [Deployment download](sc-download-deployment#deployment-download). This option is _not_ available for [Legacy download](sc-download-legacy).

* Scoring Code _doesn't_ support Prediction Explanations for time series models.

* Scoring Code _only_ supports [XEMP-based](xemp-pe) prediction explanations. [SHAP-based](shap-pe) prediction explanations aren't supported.